{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hourly traffic volume prediction on Interstate 94\n",
    "\n",
    "### Multivariate time series prediction with getML\n",
    "\n",
    "In this tutorial, we demonstrate a time series application of getML. We predict the hourly traffic volume on I-94 westbound from Minneapolis-St Paul.\n",
    "We benchmark our results against [Facebook's Prophet](https://facebook.github.io/prophet/). getML's relational learning algorithms outperform Prophet's classical time series approach by ~15%.\n",
    "\n",
    "Summary:\n",
    "\n",
    "- Prediction type: __Regression model__\n",
    "- Domain: __Transportation__\n",
    "- Prediction target: __Hourly traffic volume__\n",
    "- Source data: __Multivariate time series, 5 components__\n",
    "- Population size: __24096__\n",
    "\n",
    "_Author: Sören Nikolaus_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Background\n",
    "\n",
    "The dataset features some particularly interesting characteristics common for time series, which classical models may struggle to deal with appropriately. Such characteristics are:\n",
    "\n",
    "- High frequency (hourly)\n",
    "- Dependence on irregular events (holidays)\n",
    "- Strong and overlapping cycles (daily, weekly)\n",
    "- Anomalies\n",
    "- Multiple seasonalities\n",
    "\n",
    "\n",
    "The analysis is built on top of a dataset provided by the [MN Department of Transportation](https://www.dot.state.mn.us), with some data preparation done by [John Hogue](https://github.com/dreyco676/Anomaly_Detection_A_to_Z/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get started with the analysis and set-up your session:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "getML API version: 1.4.0\n",
      "\n",
      "getML engine is already running.\n",
      "Loading pipelines... 100% |██████████| [elapsed: 00:01, remaining: 00:00]          \n",
      "\n",
      "Connected to project 'interstate94'\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import Image\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import getml\n",
    "\n",
    "print(f\"getML API version: {getml.__version__}\\n\")\n",
    "\n",
    "getml.engine.launch()\n",
    "getml.engine.set_project(\"interstate94\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Loading data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Download from source\n",
    "\n",
    "Downloading the raw data and convert it into a prediction ready format takes time. To get to the getML model building as fast as possible, we prepared the data for you and excluded the code from this notebook. It is made available in the example notebook featuring the full analysis. We only include data after 2016 and introduced a fixed train/test split at 80% of the available data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Loading traffic...\n",
      " 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n"
     ]
    }
   ],
   "source": [
    "traffic = getml.datasets.load_interstate94(roles=False, units=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset comes with its own seasonal components. However, we choose not to use them,\n",
    "because we want to demonstrate our seasonal preprocessor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "        <th>0</th>\n",
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       "            <td class=\"time_stamp\">2016-01-01</td>\n",
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       "              \n",
       "          \n",
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       "        \n",
       "          \n",
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       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
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       "              \n",
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       "              \n",
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       "            <td class=\"time_stamp\">2016-01-01 01:00:00</td>\n",
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       "          \n",
       "        \n",
       "          \n",
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       "          \n",
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       "          \n",
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       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
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       "              \n",
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       "              \n",
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       "            <td class=\"time_stamp\">2016-01-01 02:00:00</td>\n",
       "          \n",
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       "        \n",
       "          \n",
       "            <td class=\"categorical\">New Years Day</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
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       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
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       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
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       "              >\n",
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       "          \n",
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       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
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       "              >\n",
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       "          \n",
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       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2016</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "        <th>3</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2016-01-01 03:00:00</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
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       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "        \n",
       "          \n",
       "            <td class=\"categorical\">New Years Day</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
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       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
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       "          \n",
       "        \n",
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       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
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       "            <td class=\"time_stamp\">2016-01-01 04:00:00</td>\n",
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       "              <span class=\"left\">533</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
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       "              >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">New Years Day</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">4</span\n",
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       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">4</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">1</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2016</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th></th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">...</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">...</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>24091</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2018-09-30 19:00:00</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">3543</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">No holiday</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">19</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">6</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">30</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">9</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2018</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>24092</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2018-09-30 20:00:00</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">2781</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">No holiday</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">20</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">6</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">30</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">9</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2018</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>24093</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2018-09-30 21:00:00</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">2159</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">No holiday</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">21</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">6</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">30</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">9</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2018</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>24094</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2018-09-30 22:00:00</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">1450</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">No holiday</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">22</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">6</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">30</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">9</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2018</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "      <tr>\n",
       "        <th>24095</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2018-09-30 23:00:00</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align target\">\n",
       "              <span class=\"left\">954</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">No holiday</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">23</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">6</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">30</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">9</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "          \n",
       "            <td class=\"char-align unused_float\">\n",
       "              <span class=\"left\">2018</span\n",
       "              ><span class=\"right\" style=\"width: 0ch\"\n",
       "                >&nbsp;</span\n",
       "              >\n",
       "            </td>\n",
       "          \n",
       "        \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>\n",
       "\n",
       "  <p>\n",
       "    24096 rows x 8 columns<br />\n",
       "    memory usage: 1.45 MB<br />\n",
       "    name: traffic<br />\n",
       "    type: getml.DataFrame<br />\n",
       "    \n",
       "  </p>\n"
      ],
      "text/plain": [
       " name                            ds   traffic_volume   holiday                 hour        weekday            day\n",
       " role                    time_stamp           target   categorical     unused_float   unused_float   unused_float\n",
       " unit   time stamp, comparison only                                                                              \n",
       "    0                    2016-01-01             1513   New Years Day              0              4              1\n",
       "    1           2016-01-01 01:00:00             1550   New Years Day              1              4              1\n",
       "    2           2016-01-01 02:00:00              993   New Years Day              2              4              1\n",
       "    3           2016-01-01 03:00:00              719   New Years Day              3              4              1\n",
       "    4           2016-01-01 04:00:00              533   New Years Day              4              4              1\n",
       "                                ...              ...   ...                      ...            ...            ...\n",
       "24091           2018-09-30 19:00:00             3543   No holiday                19              6             30\n",
       "24092           2018-09-30 20:00:00             2781   No holiday                20              6             30\n",
       "24093           2018-09-30 21:00:00             2159   No holiday                21              6             30\n",
       "24094           2018-09-30 22:00:00             1450   No holiday                22              6             30\n",
       "24095           2018-09-30 23:00:00              954   No holiday                23              6             30\n",
       "\n",
       " name          month           year\n",
       " role   unused_float   unused_float\n",
       " unit                              \n",
       "    0              1           2016\n",
       "    1              1           2016\n",
       "    2              1           2016\n",
       "    3              1           2016\n",
       "    4              1           2016\n",
       "                 ...            ...\n",
       "24091              9           2018\n",
       "24092              9           2018\n",
       "24093              9           2018\n",
       "24094              9           2018\n",
       "24095              9           2018\n",
       "\n",
       "\n",
       "24096 rows x 8 columns\n",
       "memory usage: 1.45 MB\n",
       "type: getml.DataFrame"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic.set_role(\"ds\", getml.data.roles.time_stamp)\n",
    "traffic.set_role(\"holiday\", getml.data.roles.categorical)\n",
    "traffic.set_role(\"traffic_volume\", getml.data.roles.target)\n",
    "\n",
    "traffic"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Prepare data for getML\n",
    "\n",
    "The `getml.datasets.load_interstate94` method took care of the entire data preparation:\n",
    "* Downloads csv's from our servers into python\n",
    "* Converts csv's to getML [DataFrames]()\n",
    "* Sets [roles]() & [units]() to columns inside getML DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Data visualization__\n",
    "\n",
    "The first week of the original traffic time series is plotted below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "col_data = \"black\"\n",
    "col_getml = \"darkviolet\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xffff5b723250>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "# 2016/01/01 was a friday, we'd like to start the visualizations on a monday\n",
    "start = 72\n",
    "end = 72 + 168\n",
    "\n",
    "fig.suptitle(\n",
    "    \"Traffic volume for first full week of the training set\",\n",
    "    fontsize=14,\n",
    "    fontweight=\"bold\",\n",
    ")\n",
    "ax.plot(\n",
    "    traffic[\"ds\"].to_numpy()[start:end],\n",
    "    traffic[\"traffic_volume\"].to_numpy()[start:end],\n",
    "    color=col_data,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Traffic__: population table\n",
    "\n",
    "To allow the algorithm to capture seasonal information, we include time components (such as the day of the week) as categorical variables. Note that we could have also used getML's Seasonal preprocessor (`getml.prepreprocessors.Seasonal()`), but in this case the information was already included in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Train/test split__\n",
    "\n",
    "We use [getML's split functionality](https://docs.getml.com/latest/api/getml.data.split.html) to retrieve a lazily evaluated split column, that we can supply to the time series api below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "split = getml.data.split.time(traffic, \"ds\", test=getml.data.time.datetime(2018, 3, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split columns are columns of mere strings that can be used to subset the data by forming bolean conditions over them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "          <th>name</th>\n",
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       "        \n",
       "          <th class=\"time_stamp\">                         ds</th>\n",
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       "          <th class=\"categorical\">holiday    </th>\n",
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       "            <th class=\"sub-header\">role</th>\n",
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       "            <th class=\"sub-header\">unit</th>\n",
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       "        \n",
       "          \n",
       "            <th class=\"sub-header time_stamp\">time stamp, comparison only</th>\n",
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       "            <th class=\"sub-header target\">              </th>\n",
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       "  <tbody>\n",
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       "      <tr>\n",
       "        <th>0</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2018-03-15</td>\n",
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       "            >\n",
       "          </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">No holiday</td>\n",
       "          \n",
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       "              \n",
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       "          <td class=\"char-align numerical\">\n",
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       "            <span class=\"left\">2018</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
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       "        <th>1</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2018-03-15 01:00:00</td>\n",
       "          \n",
       "        \n",
       "          \n",
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       "            >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">No holiday</td>\n",
       "          \n",
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       "          \n",
       "            \n",
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       "              \n",
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       "          <td class=\"char-align numerical\">\n",
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       "            <span class=\"left\">15</span\n",
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       "            <span class=\"left\">2018</span\n",
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       "        <th>2</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2018-03-15 02:00:00</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">259</span\n",
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       "            >\n",
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       "          \n",
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       "            <td class=\"categorical\">No holiday</td>\n",
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       "          \n",
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       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
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       "            >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">3</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">15</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">3</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">2018</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
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       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>3</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2018-03-15 03:00:00</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">360</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">No holiday</td>\n",
       "          \n",
       "        \n",
       "          \n",
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       "              \n",
       "              \n",
       "            \n",
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       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">3</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">15</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
       "          </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">3</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
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       "          \n",
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       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">2018</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
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       "          \n",
       "        \n",
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       "    \n",
       "      <tr>\n",
       "        <th>4</th>\n",
       "        \n",
       "          \n",
       "            <td class=\"time_stamp\">2018-03-15 04:00:00</td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">910</span\n",
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       "            >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            <td class=\"categorical\">No holiday</td>\n",
       "          \n",
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       "          \n",
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       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">4</span\n",
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       "            \n",
       "              \n",
       "              \n",
       "            \n",
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       "            <span class=\"left\">3</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
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       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">15</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
       "          </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">3</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
       "            >\n",
       "          </td>\n",
       "          \n",
       "        \n",
       "          \n",
       "            \n",
       "              \n",
       "              \n",
       "            \n",
       "          <td class=\"char-align numerical\">\n",
       "            <span class=\"left\">2018</span\n",
       "            ><span class=\"right\" style=\"width: 0ch\"\n",
       "              >&nbsp;</span\n",
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       "\n",
       "  <p>\n",
       "    unknown number of rows<br />\n",
       "    \n",
       "    type: getml.data.View<br />\n",
       "    \n",
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      ],
      "text/plain": [
       "name                            ds   traffic_volume   holiday               hour        weekday            day          month\n",
       "role                    time_stamp           target   categorical   unused_float   unused_float   unused_float   unused_float\n",
       "unit   time stamp, comparison only                                                                                           \n",
       "   0                    2018-03-15              577   No holiday               0              3             15              3\n",
       "   1           2018-03-15 01:00:00              354   No holiday               1              3             15              3\n",
       "   2           2018-03-15 02:00:00              259   No holiday               2              3             15              3\n",
       "   3           2018-03-15 03:00:00              360   No holiday               3              3             15              3\n",
       "   4           2018-03-15 04:00:00              910   No holiday               4              3             15              3\n",
       " ...                           ...              ...   ...                    ...            ...            ...            ...\n",
       "\n",
       "name           year\n",
       "role   unused_float\n",
       "unit               \n",
       "   0           2018\n",
       "   1           2018\n",
       "   2           2018\n",
       "   3           2018\n",
       "   4           2018\n",
       " ...            ...\n",
       "\n",
       "\n",
       "unknown number of rows x 8 columns\n",
       "type: getml.data.View"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic[split == \"test\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Define relational model\n",
    "\n",
    "To start with relational learning, we need to specify the data model. We manually replicate the appropriate time series structure by setting time series related join conditions (`horizon`, `memory` and `allow_lagged_targets`). We use the [high-level time series api](https://docs.getml.com/latest/api/getml.data.TimeSeries.html) for this.\n",
    "\n",
    "Under the hood, the time series api abstracts away a self cross join of the population table (`traffic`) that allows getML's feature learning algorithms to learn patterns from past observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<span style='font-size: 1.2rem; font-weight: 500;'>data model</span>\n",
       "            <div style='margin-top: 15px; margin-bottom: 5px;'>\n",
       "            <div style='margin-bottom: 10px; font-size: 1rem;'>diagram</div>\n",
       "            <div style=\"height:100px;width:660px;position:relative;\"><svg height=\"90\" width=\"650\"><rect y=\"0\" x=\"0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"90\" style=\"fill:#6829c2;stroke-width:0;\" /><text y=\"73.8\" x=\"75.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\">traffic</text><rect x=\"51\" y=\"10\" rx=\"4\" ry=\"4\" width=\"48\" height=\"48\" style=\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\" /><line x1=\"67.0\" y1=\"10\" x2=\"67.0\" y2=\"58\" style=\"stroke:white;stroke-width:3\" /><line x1=\"83.0\" y1=\"10\" x2=\"83.0\" y2=\"58\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"26.0\" x2=\"99\" y2=\"26.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"51\" y1=\"42.0\" x2=\"99\" y2=\"42.0\" style=\"stroke:white;stroke-width:3\" /><rect y=\"0\" x=\"500\" rx=\"10\" ry=\"10\" width=\"150\" height=\"90\" style=\"fill:#6829c2;stroke-width:0;\" /><text y=\"73.8\" x=\"575.0\" dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\">population</text><rect x=\"551\" y=\"10\" rx=\"4\" ry=\"4\" width=\"48\" height=\"48\" style=\" fill:#6829c2;stroke:#ffffff;stroke-width:3;\" /><line x1=\"567.0\" y1=\"10\" x2=\"567.0\" y2=\"58\" style=\"stroke:white;stroke-width:3\" /><line x1=\"583.0\" y1=\"10\" x2=\"583.0\" y2=\"58\" style=\"stroke:white;stroke-width:3\" /><line x1=\"551\" y1=\"26.0\" x2=\"599\" y2=\"26.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"551\" y1=\"42.0\" x2=\"599\" y2=\"42.0\" style=\"stroke:white;stroke-width:3\" /><line x1=\"150\" y1=\"43.0\" x2=\"490\" y2=\"43.0\" style=\"stroke:#808080;;stroke-width:4\" /><polygon points=\"500, 43.0 490, 37.0 490, 49.0 \" style=\"fill:#808080;;stroke-width:0;\" /><rect y=\"10.0\" x=\"249.0\" rx=\"10\" ry=\"10\" width=\"150\" height=\"70\" style=\"fill:#6829c2;stroke-width:0;\" /><text dominant-baseline=\"middle\" text-anchor=\"middle\" fill=\"white\"><tspan y=\"30.0\" x=\"324.0\" font-size=\"7pt\" >ds &lt;= ds</tspan><tspan y=\"40.0\" x=\"324.0\" font-size=\"7pt\" >Memory: 7.0 days</tspan><tspan y=\"50.0\" x=\"324.0\" font-size=\"7pt\" >Horizon: 1.0 hours</tspan><tspan y=\"60.0\" x=\"324.0\" font-size=\"7pt\" >Lagged targets allowed</tspan></text></svg></div>\n",
       "            </div>\n",
       "\n",
       "            <div style='margin-top: 15px;'>\n",
       "            <div style='margin-bottom: 10px; font-size: 1rem;'>staging</div>\n",
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       "      \n",
       "        \n",
       "          <th class=\"int\"> </th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">data frames</th>\n",
       "        \n",
       "      \n",
       "        \n",
       "          <th class=\"str\">staging table              </th>\n",
       "        \n",
       "      \n",
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       "    \n",
       "  </thead>\n",
       "  <tbody>\n",
       "    \n",
       "      <tr>\n",
       "        <th>0</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">population</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">POPULATION__STAGING_TABLE_1</td>\n",
       "            \n",
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       "    \n",
       "      <tr>\n",
       "        <th>1</th>\n",
       "          \n",
       "            \n",
       "              <td class=\"str\">traffic</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">TRAFFIC__STAGING_TABLE_2</td>\n",
       "            \n",
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       "            \n",
       "<span style='font-size: 1.2rem; font-weight: 500;'>container</span>\n",
       "<div style='margin-top: 15px;'>\n",
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       "          \n",
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       "              <td class=\"str\">test</td>\n",
       "            \n",
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       "              <td class=\"str\">traffic</td>\n",
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       "              <td class=\"int\">4800</td>\n",
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       "              <td class=\"str\">View</td>\n",
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       "\n",
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       "    <tr>\n",
       "      \n",
       "        \n",
       "          <th class=\"int\"> </th>\n",
       "        \n",
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       "        \n",
       "          <th class=\"str\">name   </th>\n",
       "        \n",
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       "              <td class=\"str\">traffic</td>\n",
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      ],
      "text/plain": [
       "data model\n",
       "\n",
       "  population:\n",
       "    columns:\n",
       "    - holiday: categorical\n",
       "    - traffic_volume: target\n",
       "    - ds: time_stamp\n",
       "    - hour: unused_float\n",
       "    - weekday: unused_float\n",
       "    - ...\n",
       "\n",
       "    joins:\n",
       "    - right: 'traffic'\n",
       "      time_stamps: (population.ds, traffic.ds)\n",
       "      relationship: 'many-to-many'\n",
       "      memory: 604800.0\n",
       "      horizon: 3600.0\n",
       "      lagged_targets: True\n",
       "\n",
       "  traffic:\n",
       "    columns:\n",
       "    - holiday: categorical\n",
       "    - traffic_volume: target\n",
       "    - ds: time_stamp\n",
       "    - hour: unused_float\n",
       "    - weekday: unused_float\n",
       "    - ...\n",
       "\n",
       "\n",
       "container\n",
       "\n",
       "  population\n",
       "      subset   name       rows   type\n",
       "  0   test     traffic    4800   View\n",
       "  1   train    traffic   19296   View\n",
       "\n",
       "  peripheral\n",
       "      name       rows   type     \n",
       "  0   traffic   24096   DataFrame"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_series = getml.data.TimeSeries(\n",
    "    population=traffic,\n",
    "    split=split,\n",
    "    time_stamps=\"ds\",\n",
    "    horizon=getml.data.time.hours(1),\n",
    "    memory=getml.data.time.days(7),\n",
    "    lagged_targets=True,\n",
    ")\n",
    "\n",
    "time_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.Predictive modeling\n",
    "\n",
    "We loaded the data, defined the roles, units and the abstract data model. Next, we create a getML pipeline for relational learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 getML Pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- #### 2.1.1  -->\n",
    "__Set-up of feature learners, selectors & predictor__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# The Seasonal preprocessor extracts seasonal\n",
    "# components from the time stamps.\n",
    "seasonal = getml.preprocessors.Seasonal()\n",
    "\n",
    "fast_prop = getml.feature_learning.FastProp(\n",
    "    loss_function=getml.feature_learning.loss_functions.SquareLoss,\n",
    "    num_threads=1,    \n",
    "    num_features=20,\n",
    ")\n",
    "\n",
    "predictor = getml.predictors.XGBoostRegressor()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Build the pipeline__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pipe = getml.pipeline.Pipeline(\n",
    "    tags=[\"memory: 7d\", \"horizon: 1h\", \"fast_prop\"],\n",
    "    data_model=time_series.data_model,\n",
    "    preprocessors=[seasonal],\n",
    "    feature_learners=[fast_prop],\n",
    "    predictors=[predictor],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking data model...\n",
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Checking... 100% |██████████| [elapsed: 00:01, remaining: 00:00]          \n",
      "\n",
      "OK.\n",
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "FastProp: Trying 373 features... 100% |██████████| [elapsed: 00:08, remaining: 00:00]          \n",
      "FastProp: Building features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "XGBoost: Training as predictor... 100% |██████████| [elapsed: 00:01, remaining: 00:00]          \n",
      "\n",
      "Trained pipeline.\n",
      "Time taken: 0h:0m:9.246181\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre>Pipeline(data_model='population',\n",
       "         feature_learners=['FastProp'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='SquareLoss',\n",
       "         peripheral=['traffic'],\n",
       "         predictors=['XGBoostRegressor'],\n",
       "         preprocessors=['Seasonal'],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['memory: 7d', 'horizon: 1h', 'fast_prop', 'container-lj9xWy'])</pre>"
      ],
      "text/plain": [
       "Pipeline(data_model='population',\n",
       "         feature_learners=['FastProp'],\n",
       "         feature_selectors=[],\n",
       "         include_categorical=False,\n",
       "         loss_function='SquareLoss',\n",
       "         peripheral=['traffic'],\n",
       "         predictors=['XGBoostRegressor'],\n",
       "         preprocessors=['Seasonal'],\n",
       "         share_selected_features=0.5,\n",
       "         tags=['memory: 7d', 'horizon: 1h', 'fast_prop', 'container-lj9xWy'])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.fit(time_series.train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "lines_to_next_cell": 0,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "FastProp: Building features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "\n"
     ]
    },
    {
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       "          \n",
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       "              <td class=\"datetime\">2023-07-29 19:46:31</td>\n",
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       "          \n",
       "            \n",
       "              <td class=\"datetime\">2023-07-29 19:46:31</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">test</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"str\">traffic_volume</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">179.9515</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">269.631</td>\n",
       "            \n",
       "          \n",
       "            \n",
       "              <td class=\"float\">0.9816</td>\n",
       "            \n",
       "          \n",
       "      </tr>\n",
       "    \n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "    date time             set used   target                 mae        rmse   rsquared\n",
       "0   2023-07-29 19:46:31   train      traffic_volume    200.4302    299.2045     0.9768\n",
       "1   2023-07-29 19:46:31   test       traffic_volume    179.9515    269.631      0.9816"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.score(time_series.test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Studying features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Feature correlations__\n",
    "\n",
    "Correlations of the calculated features with the target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names, correlations = pipe.features.correlations()\n",
    "\n",
    "plt.subplots(figsize=(20, 10))\n",
    "\n",
    "plt.bar(names, correlations, color=col_getml)\n",
    "plt.title(\"Feature Correlations\")\n",
    "plt.xlabel(\"Features\")\n",
    "plt.ylabel(\"Correlations\")\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Feature importances__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "lines_to_next_cell": 0,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names, importances = pipe.features.importances()\n",
    "\n",
    "plt.subplots(figsize=(20, 10))\n",
    "\n",
    "plt.bar(names, importances, color=col_getml)\n",
    "plt.title(\"Feature Importances\")\n",
    "plt.xlabel(\"Features\")\n",
    "plt.ylabel(\"Importances\")\n",
    "plt.xticks(rotation=\"vertical\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Visualizing the learned features__\n",
    "\n",
    "We can also transpile the features as SQL code. Here, we show the most important feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "lines_to_next_cell": 0,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```sql\n",
       "DROP TABLE IF EXISTS \"FEATURE_1_4\";\n",
       "\n",
       "CREATE TABLE \"FEATURE_1_4\" AS\n",
       "SELECT FIRST( t2.\"traffic_volume\" ORDER BY t2.\"ds, '+1.000000 hours'\" ) AS \"feature_1_4\",\n",
       "       t1.rowid AS rownum\n",
       "FROM \"POPULATION__STAGING_TABLE_1\" t1\n",
       "INNER JOIN \"TRAFFIC__STAGING_TABLE_2\" t2\n",
       "ON 1 = 1\n",
       "WHERE t2.\"ds, '+1.000000 hours'\" <= t1.\"ds\"\n",
       "AND ( t2.\"ds, '+7.041667 days'\" > t1.\"ds\" OR t2.\"ds, '+7.041667 days'\" IS NULL )\n",
       "AND t1.\"hour( ds )\" = t2.\"hour( ds )\"\n",
       "GROUP BY t1.rowid;\n",
       "```"
      ],
      "text/plain": [
       "'DROP TABLE IF EXISTS \"FEATURE_1_4\";\\n\\nCREATE TABLE \"FEATURE_1_4\" AS\\nSELECT FIRST( t2.\"traffic_volume\" ORDER BY t2.\"ds, \\'+1.000000 hours\\'\" ) AS \"feature_1_4\",\\n       t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"TRAFFIC__STAGING_TABLE_2\" t2\\nON 1 = 1\\nWHERE t2.\"ds, \\'+1.000000 hours\\'\" <= t1.\"ds\"\\nAND ( t2.\"ds, \\'+7.041667 days\\'\" > t1.\"ds\" OR t2.\"ds, \\'+7.041667 days\\'\" IS NULL )\\nAND t1.\"hour( ds )\" = t2.\"hour( ds )\"\\nGROUP BY t1.rowid;'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_importance = pipe.features.sort(by=\"importance\")\n",
    "by_importance[0].sql"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__Plot predictions & traffic volume vs. time__\n",
    "\n",
    "We now plot the predictions against the observed values of the target for the first 7 days of the testing set. You can see that the predictions closely follows the original series. FastProp was able to identify certain patterns in the series, including:\n",
    "- Day and night separation\n",
    "- The daily commuting peeks (on weekdays)\n",
    "- The decline on weekends\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Staging... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "Preprocessing... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "FastProp: Building features... 100% |██████████| [elapsed: 00:00, remaining: 00:00]          \n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictions = pipe.predict(time_series.test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "lines_to_next_cell": 0,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xffff58044370>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "# the test set starts at 2018/03/15 – a thursday; we introduce an offset to, once again, start on a monday\n",
    "start = 96\n",
    "end = 96 + 168\n",
    "\n",
    "actual = time_series.test.population[start:end].to_pandas()\n",
    "predicted = predictions[start:end]\n",
    "\n",
    "ax.plot(actual[\"ds\"], actual[\"traffic_volume\"], color=col_data, label=\"Actual\")\n",
    "ax.plot(actual[\"ds\"], predicted, color=col_getml, label=\"Predicted\")\n",
    "fig.suptitle(\n",
    "    \"Predicted vs. actual traffic volume for first full week of testing set\",\n",
    "    fontsize=14,\n",
    "    fontweight=\"bold\",\n",
    ")\n",
    "fig.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Features\n",
    "\n",
    "The most important feature looks as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "```sql\n",
       "DROP TABLE IF EXISTS \"FEATURE_1_4\";\n",
       "\n",
       "CREATE TABLE \"FEATURE_1_4\" AS\n",
       "SELECT FIRST( t2.\"traffic_volume\" ORDER BY t2.\"ds, '+1.000000 hours'\" ) AS \"feature_1_4\",\n",
       "       t1.rowid AS rownum\n",
       "FROM \"POPULATION__STAGING_TABLE_1\" t1\n",
       "INNER JOIN \"TRAFFIC__STAGING_TABLE_2\" t2\n",
       "ON 1 = 1\n",
       "WHERE t2.\"ds, '+1.000000 hours'\" <= t1.\"ds\"\n",
       "AND ( t2.\"ds, '+7.041667 days'\" > t1.\"ds\" OR t2.\"ds, '+7.041667 days'\" IS NULL )\n",
       "AND t1.\"hour( ds )\" = t2.\"hour( ds )\"\n",
       "GROUP BY t1.rowid;\n",
       "```"
      ],
      "text/plain": [
       "'DROP TABLE IF EXISTS \"FEATURE_1_4\";\\n\\nCREATE TABLE \"FEATURE_1_4\" AS\\nSELECT FIRST( t2.\"traffic_volume\" ORDER BY t2.\"ds, \\'+1.000000 hours\\'\" ) AS \"feature_1_4\",\\n       t1.rowid AS rownum\\nFROM \"POPULATION__STAGING_TABLE_1\" t1\\nINNER JOIN \"TRAFFIC__STAGING_TABLE_2\" t2\\nON 1 = 1\\nWHERE t2.\"ds, \\'+1.000000 hours\\'\" <= t1.\"ds\"\\nAND ( t2.\"ds, \\'+7.041667 days\\'\" > t1.\"ds\" OR t2.\"ds, \\'+7.041667 days\\'\" IS NULL )\\nAND t1.\"hour( ds )\" = t2.\"hour( ds )\"\\nGROUP BY t1.rowid;'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe.features.to_sql()[pipe.features.sort(by=\"importances\")[0].name]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "getml.engine.shutdown()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Conclusion\n",
    "\n",
    "__Benchmarks against Prophet__\n",
    "\n",
    "By design, Prophet isn't capable of delivering the 1-step ahead predictions we did with getML. To retrieve a benchmark in the 1-step case nonetheless, we mimic 1-step ahead predictions through cross-validating the model on a rolling origin. This gives Prophet an advantage as all information up to the origin is incorporated when *fitting* the model and a new fit is calculated for every 1-step ahead forecast. If you are interested in the full analysis please refer to the extended version of this [notebook](getml_examples/interstate94/interstate94.ipynb).\n",
    "\n",
    "\n",
    "__Results__\n",
    "\n",
    "We have benchmarked getML against Facebook’s Prophet library on a univariate time series with strong seasonal components.\n",
    "Prophet is made for exactly these sort of data sets, so you would expect this to be a home run for Prophet. The opposite is true - getML’s relational learning algorithms outperform Prophet's 1-step ahead predictions by ~15 percentage points:\n",
    "\n",
    "* R-squared Prophet: 83.3%\n",
    "* R-squared getML: 98.1%"
   ]
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